elasticsearch/docs/reference/ingest/processors/inference.asciidoc

[role="xpack"]
[testenv="basic"]
[[inference-processor]]
=== {infer-cap} Processor

Uses a pre-trained {dfanalytics} model to infer against the data that is being 
ingested in the pipeline.


[[inference-options]]
.{infer-cap} Options
[options="header"]
|======
| Name               | Required  | Default                        | Description
| `model_id`         | yes       | -                              | (String) The ID of the model to load and infer against.
| `target_field`     | no        | `ml.inference.<processor_tag>` | (String) Field added to incoming documents to contain results objects.
| `field_map`        | yes       | -                              | (Object) Maps the document field names to the known field names of the model. This mapping takes precedence over any default mappings provided in the model configuration.
| `inference_config` | yes       | -                              | (Object) Contains the inference type and its options. There are two types: <<inference-processor-regression-opt,`regression`>> and <<inference-processor-classification-opt,`classification`>>.
include::common-options.asciidoc[]
|======


[source,js]
--------------------------------------------------
{
  "inference": {
    "model_id": "flight_delay_regression-1571767128603",
    "target_field": "FlightDelayMin_prediction_infer",
    "field_map": {},
    "inference_config": { "regression": {} }
  }
}
--------------------------------------------------
// NOTCONSOLE


[discrete]
[[inference-processor-regression-opt]]
==== {regression-cap} configuration options

`results_field`::
(Optional, string)
Specifies the field to which the inference prediction is written. Defaults to 
`predicted_value`.

`num_top_feature_importance_values`::::
(Optional, integer)
Specifies the maximum number of
{ml-docs}/dfa-regression.html#dfa-regression-feature-importance[feature
importance] values per document. By default, it is zero and no feature importance
calculation occurs.


[discrete]
[[inference-processor-classification-opt]]
==== {classification-cap} configuration options

`results_field`::
(Optional, string)
The field that is added to incoming documents to contain the inference prediction. Defaults to 
`predicted_value`.

`num_top_classes`::
(Optional, integer) 
Specifies the number of top class predictions to return. Defaults to 0.

`top_classes_results_field`::
(Optional, string) 
Specifies the field to which the top classes are written. Defaults to 
`top_classes`.

`num_top_feature_importance_values`::::
(Optional, integer)
Specifies the maximum number of
{ml-docs}/dfa-classification.html#dfa-classification-feature-importance[feature
importance] values per document. By default, it is zero and no feature 
importance calculation occurs.


[discrete]
[[inference-processor-config-example]]
==== `inference_config` examples

[source,js]
--------------------------------------------------
{
  "inference_config": {
    "regression": {
      "results_field": "my_regression"
    }
  }
}
--------------------------------------------------
// NOTCONSOLE

This configuration specifies a `regression` inference and the results are 
written to the `my_regression` field contained in the `target_field` results 
object.


[source,js]
--------------------------------------------------
{
  "inference_config": {
    "classification": {
      "num_top_classes": 2,
      "results_field": "prediction",
      "top_classes_results_field": "probabilities"
    }
  }
}
--------------------------------------------------
// NOTCONSOLE

This configuration specifies a `classification` inference. The number of 
categories for which the predicted probabilities are reported is 2 
(`num_top_classes`). The result is written to the `prediction` field and the top 
classes to the `probabilities` field. Both fields are contained in the 
`target_field` results object.


[discrete]
[[inference-processor-feature-importance]]
==== {feat-imp-cap} object mapping

Update your index mapping of the {feat-imp} result field as you can see below to 
get the full benefit of aggregating and searching for 
{ml-docs}/dfa-classification.html#dfa-classification-feature-importance[{feat-imp}].

[source,js]
--------------------------------------------------
"ml.inference.feature_importance": {
  "type": "nested",
  "dynamic": true,
  "properties": {
    "feature_name": {
      "type": "keyword"
    },
    "importance": {
      "type": "double"
    }
  }
}
--------------------------------------------------
// NOTCONSOLE

The mapping field name for {feat-imp} is compounded as follows:

`<ml.inference.target_field>`.`<inference.tag>`.`feature_importance`

If `inference.tag` is not provided in the processor definition, it is not part 
of the field path. The `<ml.inference.target_field>` defaults to `ml.inference`.

For example, you provide a tag `foo` in the definition as you can see below:

[source,js]
--------------------------------------------------
{
  "tag": "foo",
  ...
}
--------------------------------------------------
// NOTCONSOLE


The `{feat-imp}` value is written to the `ml.inference.foo.feature_importance` 
field.

You can also specify a target field as follows:

[source,js]
--------------------------------------------------
{
  "tag": "foo",
  "target_field": "my_field"
}
--------------------------------------------------
// NOTCONSOLE

In this case, `{feat-imp}` is exposed in the 
`my_field.foo.feature_importance` field.